Package org.apache.mahout.common.distance

Examples of org.apache.mahout.common.distance.ManhattanDistanceMeasure

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    return new ClusterClassifier(models);
  }
 
  private static ClusterClassifier newSoftClusterClassifier() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new SoftCluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new SoftCluster(new DenseVector(2), 1, measure));
    models.add(new SoftCluster(new DenseVector(2).assign(-1), 2, measure));
    return new ClusterClassifier(models);
  }
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  }
 
  @Test
  public void testCanopyClassification() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new Canopy(new DenseVector(2).assign(1), 0, measure));
    models.add(new Canopy(new DenseVector(2), 1, measure));
    models.add(new Canopy(new DenseVector(2).assign(-1), 2, measure));
    ClusterClassifier classifier = new ClusterClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
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  }
 
  @Test(expected = UnsupportedOperationException.class)
  public void testMSCanopyClassification() {
    List<Cluster> models = Lists.newArrayList();
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(1), 0, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2), 1, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(-1), 2, measure));
    ClusterClassifier classifier = new ClusterClassifier(models);
    classifier.classify(new DenseVector(2));
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  @Test
  public void testRunKMeansIterationConvergesInOneRunWithGivenDistanceThreshold() {
    double[][] rawPoints = { {0,0}, {0,0.25}, {0,0.75}, {0, 1}};
    List<Vector> points = getPoints(rawPoints);

    ManhattanDistanceMeasure distanceMeasure = new ManhattanDistanceMeasure();
    List<Cluster> clusters = Arrays.asList(
        new Cluster(points.get(0), 0, distanceMeasure),
        new Cluster(points.get(3), 3, distanceMeasure));

    // To converge in a single run, the given distance threshold should be greater than or equal to 0.125,
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    ClusteringTestUtils.writePointsToFile(points, new Path(pointsPath, "file1"), fs, conf);
    ClusteringTestUtils.writePointsToFile(points, new Path(pointsPath, "file2"), fs, conf);

    Path outputPath = getTestTempDirPath("output");
    // now run the Canopy job
    CanopyDriver.run(conf, pointsPath, outputPath, new ManhattanDistanceMeasure(), 3.1, 2.1, false, false);

    // now run the KMeans job
    KMeansDriver.run(pointsPath,
                     new Path(outputPath, "clusters-0-final"),
                     outputPath,
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    initialize();
    this.setTitle("k-Means Clusters (>" + (int) (significance * 100) + "% of population)");
  }
 
  public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);
    HadoopUtil.delete(conf, output);
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    RandomUtils.useTestSeed();
    generateSamples();
    writeSampleData(samples);
    //boolean b = true;
    //if (b) {
    CanopyDriver.buildClusters(conf, samples, output, new ManhattanDistanceMeasure(), T1, T2, 0, true);
    loadClusters(output, new PathFilter() {
      @Override
      public boolean accept(Path path) {
        String pathString = path.toString();
        return pathString.contains("/clusters-");
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    plotSampleData((Graphics2D) g);
    plotClusters((Graphics2D) g);
  }
 
  public static void main(String[] args) throws Exception {
    DistanceMeasure measure = new ManhattanDistanceMeasure();
   
    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);
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      mark.serializeBenchmark();
      mark.deserializeBenchmark();
      mark.distanceMeasureBenchmark(new CosineDistanceMeasure());
      mark.distanceMeasureBenchmark(new SquaredEuclideanDistanceMeasure());
      mark.distanceMeasureBenchmark(new EuclideanDistanceMeasure());
      mark.distanceMeasureBenchmark(new ManhattanDistanceMeasure());
      mark.distanceMeasureBenchmark(new TanimotoDistanceMeasure());
     
      log.info("\n{}", mark.summarize());
    } catch (OptionException e) {
      CommandLineUtil.printHelp(group);
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  public DistanceMeasureClusterDistribution() {
  }

  public DistanceMeasureClusterDistribution(VectorWritable modelPrototype) {
    super(modelPrototype);
    this.measure = new ManhattanDistanceMeasure();
  }
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Related Classes of org.apache.mahout.common.distance.ManhattanDistanceMeasure

  • mia.clustering.ch09.NewsFuzzyKMeansClustering
  • org.apache.mahout.benchmark.VectorBenchmarks
  • org.apache.mahout.clustering.canopy.DisplayCanopy
  • org.apache.mahout.clustering.canopy.TestCanopyCreation
  • org.apache.mahout.clustering.classify.ClusterClassificationDriverTest
  • org.apache.mahout.clustering.dirichlet.models.DistanceMeasureClusterDistribution
  • org.apache.mahout.clustering.display.DisplayCanopy
  • org.apache.mahout.clustering.display.DisplayFuzzyKMeans
  • org.apache.mahout.clustering.display.DisplayKMeans
  • org.apache.mahout.clustering.display.DisplaySpectralKMeans
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